Routine baseline clinical data do not reliably predict poor first-year myopia control with low-dose atropine: a...

The aim of this study was to determine whether routinely recorded baseline clinical factors, alone or combined with machine-learning predictive modeling, can provide individual risk stratification for myopia control by identifying children who will progress more than 0.25 diopters (D) during their first year of low-dose atropine treatment.

From a 555-child source cohort treated initially with atropine 0.01% or 0.02%, 468 children had qualifying 9- to 15-month follow-up; 427 children started atropine monotherapy without a baseline optical adjunct. A child was classified as poorly responsive if either treated eye progressed more than 0.25 D of spherical equivalent refraction from baseline to the qualifying visit. This threshold was a practical clinical refraction boundary and was tested in sensitivity analyses using a 0.50-D threshold, annualized progression, and an annualized >0.50 D/year threshold. A prevalence reference, age-plus-refraction logistic regression, elastic-net regression, gradient boosting, and an exploratory multilayer-perceptron neural network were compared by repeated cross-validation.

Of 427 children, 159 (37%) were poorly responsive. Discrimination was poor and machine learning provided no improvement: the area under the curve (AUC) was 0.60 for logistic regression and 0.57 for gradient boosting [difference -0.03; 95% confidence interval (CI) -0.06 to 0.01]. Age alone performed similarly (AUC 0.60). No threshold was clinically useful; a high-sensitivity rule flagged 85% of children to identify 88% of poor responders, with positive predictive value 0.39.

Routinely recorded baseline factors did not provide clinically useful individual-level prediction of the first-year progression category. With this predictor set, observing early on-treatment progression remains the practical way to identify children who may need escalation.